Secondly, learning methods allow spatial machine intelligence systems to learn from past experience, and actively exploit new information. By building a generic data-driven model, it avoids human effort on specifying the full knowledge about mathematical and physical rules[1]}, to solve domain specific problem, before being deployed. This ability potentially enables learning machines to automatically discover new computational solutions, further develop themselves and improve their models, within new scenarios or confronting new circumstances. A good example is that by using novel view synthesis as a self-supervision signal, self-motion and depth can be recovered from unlabelled videos[2]}, [3]}.
In addition, the learned representations can further support high-level tasks, such as path planning[4]}, and decision making[5]}, by constructing task-driven maps.
